Continuous longitudinal fetus brain atlas construction via implicit neural representation

TL;DR

This paper introduces a novel deep-learning framework using implicit neural representation to construct continuous, noise-free longitudinal fetal brain atlases, improving temporal consistency and enabling finer 4D brain development analysis.

Contribution

The proposed method uniquely models a continuous 4D fetal brain atlas, addressing temporal inconsistency in existing discrete atlases with a multi-stage deep-learning approach.

Findings

Significantly improved temporal consistency of fetal brain atlases

Maintained high-quality brain structure representation

Enabled generation of finer 4D atlases with enhanced spatial and temporal resolution

Abstract

Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time inconsistency issue as a 4D (3D brain volume + 1D age) image data denoising task. Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate. Experimental results on two public fetal brain atlases (CRL and FBA-Chinese atlases) show that the proposed method can significantly improve the atlas temporal consistency while maintaining good fetus brain structure representation. In addition, the continuous longitudinal fetus brain atlases can also be extensively applied to generate finer 4D atlases in both spatial and temporal resolution.